scholarly journals The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

2020 ◽  
Author(s):  
Nicola Bodini ◽  
Mike Optis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Power Law ◽  
Wind Farm ◽  
Sonic Anemometer ◽  
Model Performance ◽  
Learning Model ◽  
Round Robin ◽  
Wind Speeds ◽  
Machine Learning Model

Abstract. The extrapolation of wind speeds measured at a meteorological mast to wind turbine hub heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power law and logarithmic profile. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the hub-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power law and log profile, which, by contrast, are not highly tuned to hub-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where hub-height winds are known; rather, its performance at nearby locations (i.e., across a wind farm site) without hub-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random forest machine-learning model is trained and evaluated at different sites and then compared against the power law and logarithmic profile. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50–100 kilometers apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power law and logarithmic profile, respectively. Using the round-robin approach proposed here, this improvement drops to 19 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based, wind-speed extrapolation methods.

scholarly journals The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

2020 ◽  
Vol 5 (2) ◽  
pp. 489-501 ◽  
Author(s):  
Nicola Bodini ◽  
Mike Optis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Wind Energy ◽  
Power Law ◽  
Wind Farm ◽  
Model Performance ◽  
Learning Model ◽  
Round Robin ◽  
Wind Speeds ◽  
Machine Learning Model

Abstract. The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.

scholarly journals New methods to improve the vertical extrapolation of near-surface offshore wind speeds

2021 ◽  
Author(s):  
Mike Optis ◽  
Nicola Bodini ◽  
Mithu Debnath ◽  
Paula Doubrawa
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Model Performance ◽  
Learning Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Near Surface ◽  
Wind Speeds ◽  
Machine Learning Model ◽  
Surface Measurements

Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in U.S. Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air-sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10-m wind speeds from spatially resolved satellite-based wind atlases.

scholarly journals New methods to improve the vertical extrapolation of near-surface offshore wind speeds

2021 ◽  
Vol 6 (3) ◽  
pp. 935-948
Author(s):  
Mike Optis ◽  
Nicola Bodini ◽  
Mithu Debnath ◽  
Paula Doubrawa
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Model Performance ◽  
Learning Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Near Surface ◽  
Wind Speeds ◽  
Machine Learning Model ◽  
Surface Measurements

Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.

scholarly journals Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study

10.2196/23454 ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. e23454
Author(s):  
Yen Po Harvey Chin ◽  
Wenyu Song ◽  
Chia En Lien ◽  
Chang Ho Yoon ◽  
Wei-Chen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medication Error ◽  
Model Performance ◽  
Learning Model ◽  
Study Cohort ◽  
Learning Approach ◽  
Hospital Data ◽  
General Internal ◽  
Machine Learning Model ◽  
Outpatient Prescriptions

Background Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan’s local databases (TLD) to address this issue. However, the international transferability of this model is unclear. Objective This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model. Methods The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance. Results The interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model. Conclusions Our ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.

scholarly journals Weather prediction using random forest machine learning model

2021 ◽  
Vol 22 (2) ◽  
pp. 1208
Author(s):  
R. Meenal ◽  
Prawin Angel Michael ◽  
D. Pamela ◽  
E. Rajasekaran
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Random Forest ◽  
Solar Radiation ◽  
Regression Models ◽  
Tamil Nadu ◽  
Weather Prediction ◽  
Learning Model ◽  
Statistical Regression ◽  
Machine Learning Model

The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.

Random Forest Machine Learning Model for Predicting Combustion Feedback Information of a Natural Gas Spark Ignition Engine

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Jinlong Liu ◽  
Christopher Ulishney ◽  
Cosmin Emil Dumitrescu
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Natural Gas ◽  
Engine Performance ◽  
Cost Effective ◽  
Learning Model ◽  
Operating Conditions ◽  
Control Variables ◽  
Feedback Information ◽  
Machine Learning Model

Abstract Engine calibration requires detailed feedback information that can reflect the combustion process as the optimized objective. Indicated mean effective pressure (IMEP) is such an indicator describing an engine’s capacity to do work under different combinations of control variables. In this context, it is of interest to find cost-effective solutions that will reduce the number of experimental tests. This paper proposes a random forest machine learning model as a cost-effective tool for optimizing engine performance. Specifically, the model estimated IMEP for a natural gas spark ignited engine obtained from a converted diesel engine. The goal was to develop an economical and robust tool that can help reduce the large number of experiments usually required throughout the design and development of internal combustion engines. The data used for building such correlative model came from engine experiments that varied the spark advance, fuel-air ratio, and engine speed. The inlet conditions and the coolant/oil temperature were maintained constant. As a result, the model inputs were the key engine operation variables that affect engine performance. The trained model was shown to be able to predict the combustion-related feedback information with good accuracy (R2 ≈ 0.9 and MSE ≈ 0). In addition, the model accurately reproduced the effect of control variables on IMEP, which would help narrow the choice of operating conditions for future designs of experiment. Overall, the machine learning approach presented here can provide new chances for cost-efficient engine analysis and diagnostics work.

scholarly journals Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial

Gut ◽  
2021 ◽  
pp. gutjnl-2021-324060
Author(s):  
Raghav Sundar ◽  
Nesaretnam Barr Kumarakulasinghe ◽  
Yiong Huak Chan ◽  
Kazuhiro Yoshida ◽  
Takaki Yoshikawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Random Forest ◽  
Survival Benefit ◽  
Validation Cohort ◽  
External Validation ◽  
Gene Signature ◽  
Learning Model ◽  
Phase Iii ◽  
Machine Learning Model

ObjectiveTo date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2×2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1), Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery.DesignThe primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort.ResultsFrom the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant. In the Pac-UFT validation cohort, Pac-Sensitive patients exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac-Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS 147 days vs 112 days, HR 0.48, p=0.022).ConclusionUsing machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit.Trial registration numberUMIN Clinical Trials Registry: C000000082 (SAMIT); ClinicalTrials.gov identifier, 02628951 (South Korean trial)

scholarly journals PCirc: random forest-based plant circRNA identification software

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shuwei Yin ◽  
Xiao Tian ◽  
Jingjing Zhang ◽  
Peisen Sun ◽  
Guanglin Li
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Circular Rna ◽  
Learning Model ◽  
Open Reading Frames ◽  
Machine Learning Techniques ◽  
Rna Seq ◽  
Random Forest Algorithm ◽  
Machine Learning Model ◽  
Identification Software

Abstract Background Circular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an important role in gene regulation. Therefore, identifying circRNAs from increasing amounts of RNA-seq data is very important. However, traditional circRNA recognition methods have limitations. In recent years, emerging machine learning techniques have provided a good approach for the identification of circRNAs in animals. However, using these features to identify plant circRNAs is infeasible because the characteristics of plant circRNA sequences are different from those of animal circRNAs. For example, plants are extremely rich in splicing signals and transposable elements, and their sequence conservation in rice, for example is far less than that in mammals. To solve these problems and better identify circRNAs in plants, it is urgent to develop circRNA recognition software using machine learning based on the characteristics of plant circRNAs. Results In this study, we built a software program named PCirc using a machine learning method to predict plant circRNAs from RNA-seq data. First, we extracted different features, including open reading frames, numbers of k-mers, and splicing junction sequence coding, from rice circRNA and lncRNA data. Second, we trained a machine learning model by the random forest algorithm with tenfold cross-validation in the training set. Third, we evaluated our classification according to accuracy, precision, and F1 score, and all scores on the model test data were above 0.99. Fourth, we tested our model by other plant tests, and obtained good results, with accuracy scores above 0.8. Finally, we packaged the machine learning model built and the programming script used into a locally run circular RNA prediction software, Pcirc (https://github.com/Lilab-SNNU/Pcirc). Conclusion Based on rice circRNA and lncRNA data, a machine learning model for plant circRNA recognition was constructed in this study using random forest algorithm, and the model can also be applied to plant circRNA recognition such as Arabidopsis thaliana and maize. At the same time, after the completion of model construction, the machine learning model constructed and the programming scripts used in this study are packaged into a localized circRNA prediction software Pcirc, which is convenient for plant circRNA researchers to use.

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